Quantification of experimental evidence is an important aspect of modern life science. In microscopy, this causes a shift from pure presentation of "supporting cases" toward the quantification of the processes under study. Computer image processing breaks through the light microcopy diffraction limit, it allows to track individual molecules in the life specimen, quantify distribution and co-localization of compartment markers, etc. The quantified experimental data forms a basis for the models of the biological processes. Quality of predictive models is crucially dependent on the accuracy of the quantified experimental data. The quality of experimental data is a function of algorithms as well as the imperfections of the "wet" experiment. The number of research papers devoted to the algorithms of microcopy image analysis, segmentation, classification and tracking has grown very fast in the last two decades. The analysis of the source of noise in "wet" biology and microscopy has gotten less attention. In this talk I will focus on the correction of experimental data before applying analysis algorithms. These corrections have two faces. They are obligatory to compensate for imperfections of "wet" microscopy while at the same time this correction can break some assumptions, which form the basis of algorithms for subsequent analysis. The examples of the different approaches for "pre-" and "post-" correction will be presented.